pythonCopy code
import pandas as pd

# Load the Excel file and read the names of the columns
file_path = '/mnt/data/DataForTable2.1WHR2023.xls'
xl = pd.ExcelFile(file_path)

# Get the names of the sheets in the Excel file
sheet_names = xl.sheet_names

# Read the first sheet (assuming data is in the first sheet) to get the column names
df_preview = pd.read_excel(file_path, sheet_name=sheet_names[0], nrows=5)

# Display the column names
df_preview.columns.tolist()

pythonCopy code
# Load the entire dataset to count the unique country names
df = pd.read_excel(file_path, sheet_name=sheet_names[0])

# Count the unique country names
unique_countries = df['Country name'].unique()
num_unique_countries = len(unique_countries)
num_unique_countries

pythonCopy code
import matplotlib.pyplot as plt
import seaborn as sns

# Filter the most recent year's data
most_recent_year = df['year'].max()
df_recent = df[df['year'] == most_recent_year]

# Sort countries by Life Ladder score
df_sorted = df_recent.sort_values(by='Life Ladder', ascending=False)

# Plotting
plt.figure(figsize=(12, 20))
sns.barplot(x='Life Ladder', y='Country name', data=df_sorted, palette='viridis')
plt.title(f'Life Ladder Scores by Country for the Year {most_recent_year}')
plt.xlabel('Life Ladder Score')
plt.ylabel('Country Name')
plt.grid(True, linestyle='--')
plt.show()

pythonCopy code
# Select the top 10 countries with the lowest Life Ladder scores for the most recent year
df_lowest_life_ladder = df_sorted.tail(10)

# Display the selected countries
df_lowest_life_ladder[['Country name', 'Life Ladder']]

pythonCopy code
# Correlation analysis for the countries with the lowest Life Ladder scores
correlation_matrix = df_lowest_life_ladder.corr()

# Plotting the correlation matrix
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix of Different Metrics for Countries with Lowest Life Ladder Scores')
plt.show()

pythonCopy code
# Filter the dataset for the selected countries
selected_countries = ['Hong Kong', 'Myanmar', 'South Korea', 'Afghanistan', 'Vietnam']
df_selected_countries = df[df['Country name'].isin(selected_countries)]

# Plotting the time series for Life Ladder scores for the selected countries
plt.figure(figsize=(14, 8))
sns.lineplot(x='year', y='Life Ladder', hue='Country name', data=df_selected_countries, marker='o')
plt.title('Time Series of Life Ladder Scores for Selected Countries')
plt.xlabel('Year')
plt.ylabel('Life Ladder Score')
plt.grid(True, linestyle='--')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

pythonCopy code
# Calculate the change in Life Ladder scores for each country over the years
df_change = df.groupby('Country name')['Life Ladder'].agg(['max', 'min'])
df_change['change'] = df_change['max'] - df_change['min']

# Sort the countries by the largest change in Life Ladder scores
df_change_sorted = df_change.sort_values(by='change', ascending=False).head(5)
df_change_sorted.reset_index(inplace=True)
df_change_sorted

pythonCopy code
# Filter the dataset for the countries with the largest changes in Life Ladder scores
largest_change_countries = df_change_sorted['Country name'].tolist()
df_largest_change = df[df['Country name'].isin(largest_change_countries)]

# Plotting the time series for Life Ladder scores for these countries
plt.figure(figsize=(14, 8))
sns.lineplot(x='year', y='Life Ladder', hue='Country name', data=df_largest_change, marker='o')
plt.title('Time Series of Life Ladder Scores for Countries with Largest Changes')
plt.xlabel('Year')
plt.ylabel('Life Ladder Score')
plt.grid(True, linestyle='--')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

pythonCopy code
# Calculate the correlation matrix for the entire dataset
correlation_matrix_all = df.corr()

# Plotting the correlation matrix for key indicators
key_indicators = ['Life Ladder', 'Log GDP per capita', 'Social support', 'Healthy life expectancy at birth',
                  'Freedom to make life choices', 'Generosity', 'Perceptions of corruption', 'Positive affect', 
                  'Negative affect']

plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix_all.loc[key_indicators, key_indicators], annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix of Key Indicators')
plt.show()

pythonCopy code
# 1. Scatter Plot for 'Life Ladder' and 'Log GDP per capita'
plt.figure(figsize=(10, 6))
sns.scatterplot(x='Log GDP per capita', y='Life Ladder', data=df, hue='year', palette='viridis', alpha=0.7)
plt.title('Scatter Plot of Life Ladder vs Log GDP per capita')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.colorbar(label='Year')
plt.show()

# 2. Line Graph for 'Life Ladder' and 'Log GDP per capita' for some representative countries
representative_countries = ['United States', 'India', 'China', 'Norway', 'South Africa']
df_representative = df[df['Country name'].isin(representative_countries)]
plt.figure(figsize=(14, 8))
sns.lineplot(x='Log GDP per capita', y='Life Ladder', hue='Country name', data=df_representative, marker='o')
plt.title('Line Graph of Life Ladder vs Log GDP per capita for Representative Countries')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, linestyle='--')
plt.tight_layout()
plt.show()

# 3. Bubble Chart for 'Life Ladder', 'Log GDP per capita', and 'Healthy life expectancy at birth'
plt.figure(figsize=(10, 6))
sns.scatterplot(x='Log GDP per capita', y='Life Ladder', size='Healthy life expectancy at birth', 
                sizes=(20, 400), hue='Healthy life expectancy at birth', data=df, palette='magma', alpha=0.7)
plt.title('Bubble Chart of Life Ladder, Log GDP per capita, and Healthy life expectancy')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.colorbar(label='Healthy life expectancy at birth')
plt.show()

pythonCopy code
# Fix the issue and recreate the visualizations

# 1. Scatter Plot for 'Life Ladder' and 'Log GDP per capita'
plt.figure(figsize=(10, 6))
scatter = sns.scatterplot(x='Log GDP per capita', y='Life Ladder', data=df, hue='year', palette='viridis', alpha=0.7)
plt.title('Scatter Plot of Life Ladder vs Log GDP per capita')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.colorbar(scatter.collections[0].colorbar.norm, label='Year')
plt.show()

# 2. Line Graph for 'Life Ladder' and 'Log GDP per capita' for some representative countries
plt.figure(figsize=(14, 8))
sns.lineplot(x='Log GDP per capita', y='Life Ladder', hue='Country name', data=df_representative, marker='o')
plt.title('Line Graph of Life Ladder vs Log GDP per capita for Representative Countries')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, linestyle='--')
plt.tight_layout()
plt.show()

# 3. Bubble Chart for 'Life Ladder', 'Log GDP per capita', and 'Healthy life expectancy at birth'
plt.figure(figsize=(10, 6))
sns.scatterplot(x='Log GDP per capita', y='Life Ladder', size='Healthy life expectancy at birth', 
                sizes=(20, 400), hue='Healthy life expectancy at birth', data=df, palette='magma', alpha=0.7)
plt.title('Bubble Chart of Life Ladder, Log GDP per capita, and Healthy life expectancy')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.colorbar(label='Healthy life expectancy at birth')
plt.show()

pythonCopy code
# Recreate the visualizations without the problematic colorbar

# 1. Scatter Plot for 'Life Ladder' and 'Log GDP per capita'
plt.figure(figsize=(10, 6))
sns.scatterplot(x='Log GDP per capita', y='Life Ladder', data=df, hue='year', palette='viridis', alpha=0.7)
plt.title('Scatter Plot of Life Ladder vs Log GDP per capita')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.legend(title='Year', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

# 2. Line Graph for 'Life Ladder' and 'Log GDP per capita' for some representative countries
plt.figure(figsize=(14, 8))
sns.lineplot(x='Log GDP per capita', y='Life Ladder', hue='Country name', data=df_representative, marker='o')
plt.title('Line Graph of Life Ladder vs Log GDP per capita for Representative Countries')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, linestyle='--')
plt.tight_layout()
plt.show()

# 3. Bubble Chart for 'Life Ladder', 'Log GDP per capita', and 'Healthy life expectancy at birth'
plt.figure(figsize=(10, 6))
sns.scatterplot(x='Log GDP per capita', y='Life Ladder', size='Healthy life expectancy at birth', 
                sizes=(20, 400), hue='Healthy life expectancy at birth', data=df, palette='magma', alpha=0.7)
plt.title('Bubble Chart of Life Ladder, Log GDP per capita, and Healthy life expectancy')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.legend(title='Healthy life expectancy at birth', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

pythonCopy code
# Define the representative countries again as the variable was lost due to the previous error
representative_countries = ['United States', 'India', 'China', 'Norway', 'South Africa']
df_representative = df[df['Country name'].isin(representative_countries)]

# Recreate the second and third visualizations

# 2. Line Graph for 'Life Ladder' and 'Log GDP per capita' for some representative countries
plt.figure(figsize=(14, 8))
sns.lineplot(x='Log GDP per capita', y='Life Ladder', hue='Country name', data=df_representative, marker='o')
plt.title('Line Graph of Life Ladder vs Log GDP per capita for Representative Countries')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, linestyle='--')
plt.tight_layout()
plt.show()

# 3. Bubble Chart for 'Life Ladder', 'Log GDP per capita', and 'Healthy life expectancy at birth'
plt.figure(figsize=(10, 6))
sns.scatterplot(x='Log GDP per capita', y='Life Ladder', size='Healthy life expectancy at birth', 
                sizes=(20, 400), hue='Healthy life expectancy at birth', data=df, palette='magma', alpha=0.7)
plt.title('Bubble Chart of Life Ladder, Log GDP per capita, and Healthy life expectancy')
plt.xlabel('Log GDP per capita')
plt.ylabel('Life Ladder')
plt.legend(title='Healthy life expectancy at birth', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()

pythonCopy code
# Check the unique regions available in the dataset
unique_regions = df['Regional indicator'].unique()
unique_regions, len(unique_regions)

pythonCopy code
# Define a mapping of countries to regions manually
region_mapping = {
    'North America': ['United States', 'Canada', 'Mexico'],
    'South America': ['Brazil', 'Argentina', 'Colombia', 'Chile', 'Peru'],
    'Europe': ['United Kingdom', 'Germany', 'France', 'Spain', 'Italy', 'Norway'],
    'Asia': ['China', 'India', 'Japan', 'South Korea', 'Indonesia'],
    'Africa': ['South Africa', 'Nigeria', 'Egypt', 'Kenya'],
    'Oceania': ['Australia', 'New Zealand']
}

# Create a new column in the DataFrame for the region each country belongs to
df['Region'] = df['Country name'].apply(lambda x: next((k for k, v in region_mapping.items() if x in v), 'Others'))

# Preview the DataFrame with the new 'Region' column
df[['Country name', 'Region']].drop_duplicates().head()

pythonCopy code
# Plot the distribution of 'Life Ladder' scores by region
plt.figure(figsize=(14, 8))
sns.boxplot(x='Region', y='Life Ladder', data=df)
plt.title('Distribution of Life Ladder Scores by Region')
plt.xlabel('Region')
plt.ylabel('Life Ladder Score')
plt.xticks(rotation=45)
plt.grid(True, linestyle='--')
plt.tight_layout()
plt.show()

pythonCopy code
# Calculate the average 'Life Ladder' score for each year across all countries
average_life_ladder_by_year = df.groupby('year')['Life Ladder'].mean().reset_index()

# Plot the average 'Life Ladder' score over time
plt.figure(figsize=(14, 8))
sns.lineplot(x='year', y='Life Ladder', data=average_life_ladder_by_year, marker='o')
plt.title('Average Life Ladder Score Over Time (Global)')
plt.xlabel('Year')
plt.ylabel('Average Life Ladder Score')
plt.grid(True, linestyle='--')
plt.tight_layout()
plt.show()

# Plot the 'Life Ladder' score over time for some representative countries
plt.figure(figsize=(14, 8))
sns.lineplot(x='year', y='Life Ladder', hue='Country name', data=df[df['Country name'].isin(representative_countries)], marker='o')
plt.title('Life Ladder Score Over Time for Representative Countries')
plt.xlabel('Year')
plt.ylabel('Life Ladder Score')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, linestyle='--')
plt.tight_layout()
plt.show()

pythonCopy code
# Select a subset of columns for the pairplot and correlation matrix
selected_columns = ['Life Ladder', 'Log GDP per capita', 'Social support', 'Healthy life expectancy at birth', 
                    'Freedom to make life choices', 'Generosity', 'Perceptions of corruption']

# Create a pairplot to visualize the relationships between variables
sns.pairplot(df[selected_columns], kind='scatter', diag_kind='kde')
plt.suptitle('Pairplot of Selected Variables', y=1.02)
plt.show()

# Create a correlation matrix to quantify the relationships between variables
correlation_matrix = df[selected_columns].corr()
plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix of Selected Variables')
plt.show()

pythonCopy code
# Create a correlation matrix to quantify the relationships between variables
correlation_matrix = df[selected_columns].corr()

# Plot the correlation matrix
plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix of Selected Variables')
plt.show()

pythonCopy code
from pandas import ExcelWriter
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

# Define the file paths for Excel and PDF reports
excel_report_path = '/mnt/data/Happiness_Report_Analysis.xlsx'
pdf_report_path = '/mnt/data/Happiness_Report_Analysis.pdf'

# Create an Excel writer object
with ExcelWriter(excel_report_path) as writer:
    # Write the original DataFrame to Excel
    df.to_excel(writer, sheet_name='Original Data', index=False)
    
    # Write the average 'Life Ladder' score by year to Excel
    average_life_ladder_by_year.to_excel(writer, sheet_name='Average Life Ladder by Year', index=False)
    
    # Write the correlation matrix to Excel
    correlation_matrix.to_excel(writer, sheet_name='Correlation Matrix')
    
    # Save the Excel file
    writer.save()

# Create a PDF writer object
with PdfPages(pdf_report_path) as pdf:
    # Plot and save each graph to the PDF
    
    # 1. Average 'Life Ladder' Score Over Time (Global)
    plt.figure(figsize=(14, 8))
    sns.lineplot(x='year', y='Life Ladder', data=average_life_ladder_by_year, marker='o')
    plt.title('Average Life Ladder Score Over Time (Global)')
    plt.xlabel('Year')
    plt.ylabel('Average Life Ladder Score')
    plt.grid(True, linestyle='--')
    plt.tight_layout()
    pdf.savefig()
    plt.close()
    
    # 2. Life Ladder Score Over Time for Representative Countries
    plt.figure(figsize=(14, 8))
    sns.lineplot(x='year', y='Life Ladder', hue='Country name', data=df[df['Country name'].isin(representative_countries)], marker='o')
    plt.title('Life Ladder Score Over Time for Representative Countries')
    plt.xlabel('Year')
    plt.ylabel('Life Ladder Score')
    plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
    plt.grid(True, linestyle='--')
    plt.tight_layout()
    pdf.savefig()
    plt.close()
    
    # 3. Correlation Matrix of Selected Variables
    plt.figure(figsize=(10, 6))
    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
    plt.title('Correlation Matrix of Selected Variables')
    pdf.savefig()
    plt.close()

# Return the file paths for download
excel_report_path, pdf_report_path

pythonCopy code
from docx import Document
from docx.shared import Inches

# Initialize a Document
doc = Document()

# Title
doc.add_heading('Happiness Report Analysis', 0)

# Introduction
doc.add_heading('1. Introduction', level=1)
doc.add_paragraph(
    "This report provides an in-depth analysis of the World Happiness Report data. "
    "The aim is to uncover insights into factors that contribute to happiness across different countries and regions. "
    "Various metrics such as GDP per capita, social support, and perceptions of corruption are considered."
)

# Methodology
doc.add_heading('2. Methodology', level=1)
doc.add_paragraph(
    "The analysis is based on data exploration, statistical analysis, and data visualization techniques. "
    "Specifically, we examined the distribution of 'Life Ladder' scores by country and region, "
    "explored trends over time, and evaluated correlations between different variables."
)

# Key Insights
doc.add_heading('3. Key Insights', level=1)

# Economic Factors
doc.add_heading('3.1 Economic Factors', level=2)
doc.add_paragraph(
    "A strong positive correlation was observed between 'Log GDP per capita' and 'Life Ladder' scores. "
    "This indicates that economic stability and prosperity significantly impact the level of happiness in a country."
)

# Social Support
doc.add_heading('3.2 Social Support', level=2)
doc.add_paragraph(
    "Social support showed a high positive correlation with 'Life Ladder' scores. "
    "Countries with strong social support systems tend to have happier citizens."
)

# Health
doc.add_heading('3.3 Health', level=2)
doc.add_paragraph(
    "'Healthy life expectancy at birth' also showed a strong positive correlation with happiness levels. "
    "Good health contributes significantly to a higher quality of life and happiness."
)

# Regional Differences
doc.add_heading('3.4 Regional Differences', level=2)
doc.add_paragraph(
    "Significant differences in 'Life Ladder' scores were observed between regions. "
    "North America and Europe tend to have higher scores, while Africa generally has lower scores."
)

# Trends Over Time
doc.add_heading('3.5 Trends Over Time', level=2)
doc.add_paragraph(
    "The analysis of trends over time revealed varying levels of 'Life Ladder' scores among representative countries. "
    "Each country has its own unique set of factors affecting its happiness level, "
    "making it important to consider a wide range of variables for a comprehensive understanding."
)

# Conclusions
doc.add_heading('4. Conclusions', level=1)
doc.add_paragraph(
    "The analysis provides valuable insights into the factors that contribute to happiness. "
    "Economic stability, social support, and health stand out as significant contributors. "
    "The insights from this report can be useful for policymakers and organizations aiming to improve quality of life."
)

# Save the document
doc_path = '/mnt/data/Happiness_Report_Analysis.docx'
doc.save(doc_path)

doc_path

pythonCopy code
# Function to save plots as image files
def save_plot_as_image(plot, file_path):
    fig = plot.get_figure()
    fig.savefig(file_path)

# Create directory to store plots
plot_directory = '/mnt/data/plots/'
os.makedirs(plot_directory, exist_ok=True)

# List to store file paths of generated plots
plot_file_paths = []

# Generate the plots again and save them as image files

# 1. Average 'Life Ladder' Score Over Time (Global)
plot1 = sns.lineplot(x='year', y='Life Ladder', data=average_life_ladder_by_year, marker='o')
plt.title('Average Life Ladder Score Over Time (Global)')
plt.xlabel('Year')
plt.ylabel('Average Life Ladder Score')
plt.grid(True, linestyle='--')
plot1_file_path = f"{plot_directory}Average_Life_Ladder_Score_Over_Time_Global.png"
save_plot_as_image(plot1, plot1_file_path)
plot_file_paths.append(plot1_file_path)
plt.close()

# 2. Life Ladder Score Over Time for Representative Countries
plot2 = sns.lineplot(x='year', y='Life Ladder', hue='Country name', data=df[df['Country name'].isin(representative_countries)], marker='o')
plt.title('Life Ladder Score Over Time for Representative Countries')
plt.xlabel('Year')
plt.ylabel('Life Ladder Score')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, linestyle='--')
plot2_file_path = f"{plot_directory}Life_Ladder_Score_Over_Time_for_Representative_Countries.png"
save_plot_as_image(plot2, plot2_file_path)
plot_file_paths.append(plot2_file_path)
plt.close()

# 3. Correlation Matrix of Selected Variables
plot3 = sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix of Selected Variables')
plot3_file_path = f"{plot_directory}Correlation_Matrix_of_Selected_Variables.png"
save_plot_as_image(plot3, plot3_file_path)
plot_file_paths.append(plot3_file_path)
plt.close()

# Add the saved plots to the Word document
for plot_file_path in plot_file_paths:
    doc.add_picture(plot_file_path, width=Inches(5.0))

# Save the updated document with plots
doc_with_plots_path = '/mnt/data/Happiness_Report_Analysis_with_Plots.docx'
doc.save(doc_with_plots_path)

doc_with_plots_path

pythonCopy code
import os

# Function to save plots as image files
def save_plot_as_image(plot, file_path):
    fig = plot.get_figure()
    fig.savefig(file_path)

# Create directory to store plots
plot_directory = '/mnt/data/plots/'
os.makedirs(plot_directory, exist_ok=True)

# List to store file paths of generated plots
plot_file_paths = []

# Generate the plots again and save them as image files

# 1. Average 'Life Ladder' Score Over Time (Global)
plot1 = sns.lineplot(x='year', y='Life Ladder', data=average_life_ladder_by_year, marker='o')
plt.title('Average Life Ladder Score Over Time (Global)')
plt.xlabel('Year')
plt.ylabel('Average Life Ladder Score')
plt.grid(True, linestyle='--')
plot1_file_path = f"{plot_directory}Average_Life_Ladder_Score_Over_Time_Global.png"
save_plot_as_image(plot1, plot1_file_path)
plot_file_paths.append(plot1_file_path)
plt.close()

# 2. Life Ladder Score Over Time for Representative Countries
plot2 = sns.lineplot(x='year', y='Life Ladder', hue='Country name', data=df[df['Country name'].isin(representative_countries)], marker='o')
plt.title('Life Ladder Score Over Time for Representative Countries')
plt.xlabel('Year')
plt.ylabel('Life Ladder Score')
plt.legend(title='Country', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True, linestyle='--')
plot2_file_path = f"{plot_directory}Life_Ladder_Score_Over_Time_for_Representative_Countries.png"
save_plot_as_image(plot2, plot2_file_path)
plot_file_paths.append(plot2_file_path)
plt.close()

# 3. Correlation Matrix of Selected Variables
plot3 = sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix of Selected Variables')
plot3_file_path = f"{plot_directory}Correlation_Matrix_of_Selected_Variables.png"
save_plot_as_image(plot3, plot3_file_path)
plot_file_paths.append(plot3_file_path)
plt.close()

# Add the saved plots to the Word document
for plot_file_path in plot_file_paths:
    doc.add_picture(plot_file_path, width=Inches(5.0))

# Save the updated document with plots
doc_with_plots_path = '/mnt/data/Happiness_Report_Analysis_with_Plots.docx'
doc.save(doc_with_plots_path)

doc_with_plots_path